According to a technique conventionally known, a plurality of information processing devices cooperate with each other to perform one process as a plurality of events. In a system employing this technique, an event executed by an information processing device may trigger an event to be executed by a different information processing device. This makes it difficult to analyze the performance of the entire system or a cause of a trouble on the basis of a program of each information processing device.
According to a known technique of a model generating device, a model of a relationship between an event and another event triggered by the former event is generated. This makes it possible to easily analyze the performance of an entire system, or a cause of a trouble. In the example illustrated in FIG. 18, a model generating device observes communications between information processing devices contained in a system, and acquires information indicating a time period during which each information processing device executed an event.
The model generating device assumes a candidate of a relationship between an event and another event triggered by the former event on the basis of the acquired information. Then, the model generating device specifies a model indicating a relationship between an event and another event triggered by the former event by using the regularity of the assumed candidate. The model generating device thereafter compares communications between the information processing devices with the generated models. As a result, the performance of the system itself, or a cause of a trouble is easily analyzed. FIG. 18 is a diagram illustrating the model generating device.
A model generated by the model generating device is described next by using FIGS. 19 and 20. In the example illustrated in FIG. 19, IIOP 1 that is an event of IIOP (Internet Inter-ORB Protocol) is executed during execution of HTTP 1 that is an event of HTTP (Hypertext Transfer Protocol). In this example, the model generating device assumes that HTTP 1 triggered IIOP 1, and generates a model of a tree structure in which HTTP 1 as a master and IIOP 1 as a slave are associated with each other. FIG. 19 is a diagram (1) illustrating the model.
In the example illustrated in FIG. 20, IIOP 1 and IIPO 2 are executed during execution of HTTP 1. In this example, a model generating device assumes that HTTP 1 triggered two processes of IIOP 1 and IIOP 2, and generates a model of a tree structure in which HTTP 1 as a master, and IIOP 1 and IIPO 2 as slaves are associated with each other. FIG. 20 is a diagram (2) illustrating the model.
A matching process of a model performed by the model generating device is described next by using FIG. 21. FIG. 21 is a diagram illustrating the matching process. The model generating device of the example illustrated in FIG. 21 have already generated a model indicating that HTTP 1 (H1) triggered IIOP 1 (I1), and a model indicating that HTTP 2 (H2) triggered IIOP 2 (I2). In this case, the model generating device performs a matching process to detect the same relationships from assumed model candidates as those of the models already generated. As a result, the performance of an entire system, or a cause of a trouble is easily analyzed.
In the aforementioned technique of the model generating device, a candidate to be assumed will be made complex if a large number of events are executed at the same time. This requires a massive amount of calculation to generate a model, making it impossible to generate a model.
As seen from the example on the left side of FIG. 22, the model generating device can generate models in a limited time if model candidates assumed from information flowing through a network are simple. However, if a large number of events are executed at the same time, model candidates assumed from information passing through the network are made complex as seen from the example on the right side of FIG. 22. In this case, the model generating device cannot generate a model in a limited time. Herein, FIG. 22 is a diagram illustrating the volumes of relationships in data.
Patent Document: Japanese Laid-open Patent Publication No. 2006-011683